The Impact of AI-Driven Recommendation Systems on Consumer Behaviour and Decision Making: A Study of Personalization and Privacy Concerns

AI has entered the online market revolution and currently, AI-based recommendation engines are used extensively in online stores, streaming sites, and even social media as a means of providing a personal experience to the user as a way to offer online shops, streaming, and social media. The use of such systems utilizes algorithms that analyze customer data, tastes and patterns of trends in behavior to suggest the most appropriate products, services or content that would be likely to appeal to the users. The given research has a twofold impact on the consumer decision-making process due to the personalization of the system and the presence of more privacy concerns due to comprehensive data collection that is the thesis of the said research. The study will adopt a mixed method method, implying the analysis of 300 consumers and conducting secondary research on the basis of existing literature available, to explore the relationship between personalization and perceived satisfaction, trust and purchase intention and study the effects of the issue of privacy on user acceptance and purchase behavior. The conclusions presented are that tailored recommendations significantly help in consumer interest and effectiveness in decision-making, but more kprivacy threatens to trust and disclosure intentions. The findings demonstrate that businesses need to strike a balance between the principle of personalization and the principle of having a high level of data privacy in order to make the users more satisfied and also achieve the norms of ethics. The limitation of practical use is the use of self-report survey information and the possibility of regional bias in consumer response. Future studies can be conducted on how various cultures respond to cross-cultural differences, how clarifying AI enhances trust, and the long-term impacts of the recommendation systems on consumer decisions.